Shallow clustering-based approaches necessitate the hand-crafting of discriminative features and often require the selection of an appropriate kernel. Recently, with the advances in DL, there have been attempts to extend these classical approaches. Most of these methods, such as Deep SVDD [1]} variants, extend traditional methods by learning a kernel that maps data to a discriminative high-dimensional feature space. This is usually carried out by optimizing a Neural Network. These approaches have shown promising results when dealing with non-sequential data. Unfortunately, the temporal modeling of time-series is often disregarded, mainly relying on a simple sliding window. As a solution, Shen et al.[2]} suggest fusing multi-scale temporal features and employing a Recurrent Neural Networks (RNN) to model temporal dependencies.
